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Compare Where It Matters: Using Layer-Wise Regularization To Improve
  Federated Learning on Heterogeneous Data

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data

1 December 2021
Ha Min Son
M. Kim
T. Chung
    FedML
ArXivPDFHTML

Papers citing "Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data"

4 / 4 papers shown
Title
FedCC: Robust Federated Learning against Model Poisoning Attacks
FedCC: Robust Federated Learning against Model Poisoning Attacks
Hyejun Jeong
H. Son
Seohu Lee
Jayun Hyun
T. Chung
FedML
54
5
0
20 Feb 2025
Entropy-driven Fair and Effective Federated Learning
Entropy-driven Fair and Effective Federated Learning
Lung-Chuang Wang
Zhichao Wang
Sai Praneeth Karimireddy
Xiaoying Tang
Xiaoying Tang
FedML
25
9
0
29 Jan 2023
Federated Learning with Intermediate Representation Regularization
Federated Learning with Intermediate Representation Regularization
Ye Lin Tun
Chu Myaet Thwal
Yu Min Park
Seong-Bae Park
C. Hong
FedML
18
6
0
28 Oct 2022
FedBN: Federated Learning on Non-IID Features via Local Batch
  Normalization
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li
Meirui Jiang
Xiaofei Zhang
Michael Kamp
Qi Dou
OOD
FedML
168
786
0
15 Feb 2021
1